Executive Summary
In 2025 the Modern Enterprise Tech Stack is maturing. While AWS and Git are the bedrock of technical roles, Excel remains the world’s most required data tool, proving that human-readable data is still the ultimate business priority. However, the presence of Kubernetes and Power BI shows that businesses are moving toward a future that is both cloud-native and deeply integrated with the Microsoft ecosystem.
The 2025 job market shows a strong demand for professionals proficient in the following pillars of the Modern Enterprise Tech Stack:
- Excel is mentioned in 37% of job postings.
- Followed by the cloud platforms (AWS with 29%, Azure with 24% , and GCL with 14%) for storing, managing, and accessing data.
- Git(21%) for ensuring the code is versioned.
- Kubernetes(13%) and Docker(12%) for scalable software.
Introduction
This report presents an exploratory data analysis (EDA) of job posting data collected throughout 2025. The primary goal is to identify the most required tools designed to collect, store, transform, and analyze data efficiently, or tools that do the supporting role for these operations like git. These will further be refered to as tools. The precence of these tools will be analyzed in the current labor market, in order to understand how these requirements shift across different professional specializations. By analyzing the intersection of job titles and required tools, I will aim to provide some insights into the most popular tools defining the industry in the year 2025.
The initial raw data was gained from a large-scale coresignal jobs data, totaling approximately 60 million global job postings. The analyzed data set used in this report consists of 34 features and around 600 000 observations, ranging from January 1st, 2025, to December 19, 2025. For a detailed breakdown of the features, please refer to the Table 1.
Data Preparation and Cleaning
For data preparation and cleaning please refer to “Appendix 1: Preparing the data set.” It will give the detailed look into how the data frame that I will analyse here was prepared.
A preview of the analysed dataset is presented below in Table 2, Table 3, Table 4.
Observations and Features
This section begins the detailed exploration of the dataset’s structure. I will examine the characteristics of each column to ensure data integrity and understand the available information.
Specifically, for categorical features (like title, company_name, and company_industry), I’ll identify the distinct categories present and count the number of unique observations in each. This step confirms the data types and prepares us for subsequent analysis. Below you can see the breakdown of each type.
Analysed tools:
['Excel', 'Google_Sheets', 'Fivetran', 'Airbyte', 'dbt', 'Snowflake', 'BigQuery', 'Airflow']
['Prefect', 'Power_BI', 'Tableau', 'Looker', 'Git', 'Docker', 'Kubernetes', 'Terraform']
['AWS', 'Azure', 'GCP', 'Databricks', 'Kafka', 'Spark', 'Monte_Carlo']
Categorical features:
['title', 'company_name', 'company_industry', 'state', 'broad_industry_group']
Date features:
['created_at']
Job titles:
['manager', 'engineer', 'analyst', 'scientist', 'developer']
In total 23 tools are present in the data set. The categotical information about the jobs data is title, company_name, company_industry, state, broad_industry_group. The date when the job posting was created is in the created_at column. And if the particular job posting is listed for a manager, engineer, analyst, scientist, or a developer is noted in the corresponding columns.
NOTE: Keep in mind that the same job posting can be in multiple job title categories, like scientist and analyst, as well as single job posting could require experience with multiple tools.
Outliers
NOTE:
Outliers were mainly handaled in Appendix 2. Please refer to the corresponding notebook/report for more details. The function used for verifying the meaning of the words like “excel” or “airflow” utilizes the Ollama Large Language Model (LLM) to identify the meaning the given word and decide if it is tool or just a word. This means the categorization is not completely precice. This was nessesessary because word “excel” could either be an english verb or the name of a program, also some tools might have some false positives, due to the typos in the job descriptions like the word “perfect” is misspelled to “prefect”, thus LLM was employed to avoid these cases.
From the tables Table 5 and Table 6 we see there is not so much additional improvements we could do, we perhaps could fix some typos in the data but that will not be so crucial for this analyses.
From this we can see that there are 54 states in US, which includes states and US territories.
Exploratory Data Analysis
This section presents the Exploratory Data Analysis (EDA) of the 2025 US job market for tech jobs. The goal is to identify patterns and trends within the job postings. The analysis begins with a general overview of the dataset, including the total number of observations and key features. This initial phase identifies the most common tools, the primary industries hiring for tech roles, and the organizations with the highest volume of postings. We also examine the timing of these posts to identify potential hiring seasons and pinpoint which geographic states are leading in tech employment.
Distribution of Features
Understanding the foundational characteristics of the data is the first step. This part of the report covers:
Tool Popularity: An absolute and percentage-based ranking of the 23 identified tools.
Industry and Company Presence: Identification of the sectors and specific employers driving the most activity.
Temporal and Geographic Trends: A look at hiring cycles throughout 2025 and the states with the highest density of tech opportunities.
Distribution of Job Titles by Job Type
In this section, we examine the distribution of job postings across primary professional categories. Based on the data visualized in Figure 2, there is a clear and substantial demand for technical roles, particularly within the engineering domain.
Engineers represent the majority of the market share at 51%, significantly outpacing other roles. Developers and Analysts follow with 28% and 12% accordingly. Specialized roles such as Scientists and Managers constitute the remaining 4% and 5% of the postings, respectively. These findings suggest that for the 2025 hiring landscape, companies are prioritizing the foundational technical infrastructure provided by engineering talent.
engineer 302026
analyst 167770
developer 69526
manager 28194
scientist 21453
dtype: int64
Distribution of Company Names
In this section, we examine the leading organizations driving the demand for technical talent in the United States. While the tech landscape is vast and diverse, a small group of industry giants and specialized platforms accounts for a significant portion of total hiring activity.
From Figure 3, we can observe that Job via Dice maintains a commanding presence, representing 5% of the entire tech job market in the USA. This high volume highlights the platform’s role as a primary aggregator for technical specialized roles.
When analyzing the “Big Tech” sector specifically, Amazon, Microsoft, Google, and Apple all appear within the top 20 hiring entities. This data suggests that while these tech behemoths are influential, the market remains highly fragmented, with a significant amount of hiring distributed across thousands of mid-sized firms and diverse industry sectors.
Distribution of Industries
In this section, we analyze the distribution of technology-related roles across various economic sectors. Understanding where demand originates provides critical context for the current hiring landscape and identifies the primary drivers of technical growth.
Based on the data visualized in Figure 4, the Tech, Data & Telecom industry remains the dominant force, accounting for 38% of analyzed professional requirements. This sector continues to outpace all others, reinforcing its role as the primary engine for tech employment.
Outside of the core technology sector, the demand is significantly more distributed. Professional, Legal & Business Services follow with a 15% market share, while Manufacturing, Industrial & Defense represents 9% of the postings. The Finance, Insurance & Real Estate (FinTech) sector accounts for 10%, highlighting a stable need for technical expertise in modernizing financial infrastructure. Notably, all other industries each represent less than 4% of the market share (each), indicating a high concentration of tech talent within the top four sectors.
Below @dist-company-industries you can see the raw distribution of the top 20 industries that are not cleaned, just for the sake of understanding the raw data. Software development and IT industiries are absolute leaders.
Distribution of States
In this section, we analyze the geographic distribution of tech talent demand across the United States. Identifying these “hiring hubs” allows us to understand the regional concentrations of the digital economy and where companies are focusing their recruitment efforts.
Based on the data visualized in Figure 6 and Figure 7, California remains the primary driver of technical employment, accounting for 11% of analyzed job postings. Texas(8%) follows closely as the second-largest market, reinforcing its status as a significant and growing center for technology and innovation. New York has the third-largest market taking up 5% of analyzed postings. Together, these three states represent a substantial portion of the national demand.
For a more granular view of regional trends, an interactive heatmap is available in the project files at outputs/figures/tools/us_hiring_map.html or html version of this report. This interactive asset allows for state-by-state comparisons and provides specific market share details for each territory.
Distribution of Job Posting Times
In this section, we analyze the seasonal variations in hiring activity throughout 2025. Based on the data visualized in Figure 8, the hiring landscape exhibits a distinct “double peak” pattern. The highest volume of activity occurs in August (12%) and November(13%). These surges likely correspond to the conclusion of the summer period and the finalization of year-end technical projects, respectively.
Market Leader Deep Dive: FAANG and MANGO
The final part of the analysis focuses on the industry giants that often set the trend for the rest of the market. We compare two distinct groups:
FAANG (Meta, Apple, Amazon, Netflix, Google) These established leaders provide a benchmark for high-scale, mature tech environments. We analyze their core tool requirements to see which traditional technologies remain dominant.
MANGO (Microsoft, Apple, Nvidia, Google, OpenAI/Anthropic) The MANGO index represents the architects of the current AI era. This section highlights the tool preferred by companies at the forefront of hardware innovation and LLM development. Comparing these groups reveals whether AI-centric firms are pivoting toward newer tools faster than the broader market.
Key Differences
Focus: FAANG was about digital advertising, e-commerce, and content streaming. MANGO is about generative AI, cloud infrastructure, and AI hardware.
Context: While FAANG focused on user attention, MANGO focuses on AI reasoning and intelligence.
FAANG
In this section, we examine the hiring patterns of the world’s most influential technology giants. These organizations often set the standard for technical requirements and recruitment volume across the global IT sector.
Based on the data in Figure 17, Amazon emerges as the clear leader in recruitment volume among the FAANG group, accounting for 62% of analyzed job postings within this elite cohort. Google follows with 24%, while Apple represents 8% of the demand. Meta and Netflix round out the group with 5% and 2% of the market share, respectively. This distribution highlights Amazon’s expansion and its significant role as a primary employer for technical talent.
The technical stack required by these organizations (see Figure 18) reflects their heavy investment in cloud infrastructure and data-driven decision-making. The high demand for AWS (53%) and Google Cloud (24%) is directly linked to the fact that Amazon and Google are the dominant employers within this dataset, essentially hiring to build and maintain their own proprietary ecosystems.
While cloud platforms lead the requirements, the data also shows a strong reliance on traditional analytical tools. Excel (15%) and Tableau (12%) remain vital for business intelligence within these firms, proving that even at the highest level of tech innovation, foundational data tools are still used for daily operations. More specialized infrastructure tools like Spark, Kubernetes, and Terraform show lower total percentages, but they represent the critical “engine room” skills required for high-level engineering roles.
MANGO
In this section, we pivot our focus to the “MANGO” cohort—Meta, Apple, NVIDIA, Google, and Oracle—representing a more hardware and AI-centric evolution of the traditional tech giants. Analyzing this group allows us to see how the demand for talent shifts when emphasizing cutting-edge infrastructure and silicon innovation.
According to Figure 19, Google stands as the dominant hiring force in this group, responsible for 34% of the job postings. Microsoft and Apple maintain a strong presence with 19% and 17% respectively, while NVIDIA—fueled by the ongoing AI hardware boom—accounts for 15% of the requirements. Meta occupies a smaller portion of the current hiring landscape at 3%. This distribution underscores a market where established cloud and hardware leaders are currently driving the bulk of new technical opportunities.
The tool requirements within the MANGO group (see Figure 20) show a distinct shift compared to the FAANG cohort. Most notably, GCP (41%) and Azure (30%) are the leading technologies. This is due to the fact that Microsoft and Google are central to the MANGO category, while Amazon is excluded, leading to a significantly lower presence for AWS (11%).
Beyond cloud providers, the MANGO group places a much higher premium on deployment and containerization technologies. Kubernetes (19%), Git (16%), and Docker (11%) appear with higher frequency here than in other cohorts. This suggests that the work within these companies is heavily focused on scalable infrastructure and complex DevOps pipelines, likely supporting the massive compute requirements of AI development and global cloud services.
Key Insights
Excel and AWS have solidified their roles as the essential tools of the 2025 technical landscape. While Excel remains the near-universal baseline for business data management (required in 37% of postings), AWS has become the “infrastructure lingua franca,” appearing in 29% of analyzed technical roles—more than Azure and GCP combined.
The demand for Engineers is overwhelming, accounting for 51% of the market share among technical titles. This highlights a critical industry shift: companies are focusing more on building and architecting robust systems than on pure management or stand-alone data analysis.
As AI integration matures, the “Modern Data Stack” is shifting towards performance. Tools that facilitate real-time data streaming and high-speed processing, such as Kafka and Spark, are seeing increased co-occurrence with cloud platforms, indicating that “AI-ready” infrastructure is a top hiring priority.
In the battle for Business Intelligence, Power BI (13%) has gained a clear lead over Tableau (10%). This suggests that organizations are favoring the deep integration of the Microsoft ecosystem to streamline workflows from the desktop (Excel) to the cloud (Azure).
Tech hiring remains centered in established hubs, with California, Texas, and New York leading national demand. Notably, AWS dominance is strongest in these high-tech corridors, while Excel remains the leader in states with more traditional industry bases.
Proficiency in version control (Git, 21%) and containerization (Kubernetes, 13%) is no longer an “extra” skill; these have become fundamental requirements across almost all technical job categories.
Summary
The 2025 technical job market is defined by a massive reliance on cloud infrastructure, data automation, and the maturation of the “Modern Data Stack”. Through an analysis of nearly 600,000 job postings, it is evident that while new specialized tools emerge, the industry remains anchored by foundational technologies.
AWS and Git have become the bedrock of technical execution, but Excel remains the world’s most frequently required tool, proving that human-readable data remains the ultimate priority for business stakeholders. However, the rise of Kubernetes and the shift toward Power BI indicate that organizations are aggressively moving toward a future that is simultaneously cloud-native and deeply integrated into existing enterprise ecosystems.
Hiring activity remains regionally concentrated, with California and Texas leading the demand. The dominance of the “Engineer” role (51% market share) confirms that for the 2025 calendar year, companies are prioritizing the foundational technical infrastructure and the construction of scalable systems over purely analytical or management-heavy positions.
Suggestions for Further Improvements
Salary and Tool Correlation: Integrating salary data would allow for the creation of a “Value Matrix” to identify which toolsets (e.g., dbt + Snowflake) command the highest financial premium compared to more common tools like Excel.
Growth Velocity Tracking: Comparing this 2025 data against 2024 benchmarks would help identify “rising stars”—tools like dbt or Monte Carlo that may have lower total volume but high growth velocity.
We can use the tool categories from stackoverflow and compare the tendencies with our data.
Limitations
The limitations of this analyses is the data quality, we cannot be sure that we have ALL the job postings from 2025 in USA, yet I believe that the general tendencies would still remain in the data. For being comepletly precice the error rates could be calculated, yet they would bring some confusion for non technical readers, thus the errors remain unclear. As I have mentioned this would not change the general tendencies just could make some difference for the postings where the percentages are very close to eachother.
